Plan recognition is a form of abductive reasoning that involves inferring plans
that best explain sets of observed actions. Most existing approaches to plan
recognition and other abductive tasks employ either purely logical methods that
do not handle uncertainty, or purely probabilistic methods that do not handle
structured representations. To overcome these limitations, this paper
introduces an approach to abductive reasoning using a first-order probabilistic
logic, specifically Markov Logic Networks (MLNs). It introduces several novel
techniques for making MLNs efficient and effective for abduction. Experiments
on three plan recognition datasets show the benefit of our approach over
existing methods.